Selection of frequency offset for an mri scan

ABSTRACT

A frequency offset is selected based on similarity measures of multiple MRI images obtained from frequency scout measurements associated with multiple frequency offsets from a reference frequency of a magnetization excitation pulse. The similarity measure for each respective MRI image of the multiple MRI images is determined based on a comparison between at least one reference image and the respective MRI image. The at least one reference image is determined from the multiple MRI images based on spectrum information of each of the multiple MRI images. Such methods facilitate automatically determining/selecting a more optimal frequency offset for an MRI scan following a frequency scout scan, in particular, for an SSFP or a bSSFP pulse sequence, and thereby banding artifacts and/or flow-related artifacts can be reduced for the MRI scan.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims priority under 35 U.S.C. § 119 toEuropean Patent Application No. 22171819.0, filed May 5, 2022, theentire contents of which are incorporated herein by reference.

TECHNICAL FIELD

Various examples of the present invention generally relate to magneticresonance (MR) imaging (MRI). Various examples specifically relate toselecting a frequency offset for an MRI scan to reduce artifacts, e.g.,banding artifacts and/or flow-related artifacts.

BACKGROUND

In balanced steady-state free precession (balanced SSFP or bSSFP) pulsesequences commonly used for cardiac MRI, signal modulation, e.g.,banding or flow-related artifacts, due to inhomogeneity of the mainmagnetic field are frequently observed, especially at higher mainmagnetic field strengths, e.g., 3 Tesla (T), 7 T, and even 9 T. Thespatial position of these artifacts can be shifted by using amagnetization excitation pulse (or simply excitation pulse) with afrequency offset from the Larmor frequency to reduce artifacts in aregion of interest (ROI), e.g., the heart or another region underinvestigation. I.e., the actual frequency of the magnetizationexcitation pulse is offset from the Larmor frequency by a frequencyoffset.

In clinical practice, frequency scout (FS) measurements are acquired toscan the possible frequency offsets and enable visual selection theoptimal frequency offset through visual inspection of FS images. Thefrequency offset within an FS scan of an FS measurement that has theleast artifacts in the ROI is selected by a medical expert. Afterwards,the selected frequency offset is transferred to the acquisition protocoland is used for bSSFP acquisition.

Non-patent literature—Schär, Michael, et al. “Cardiac SSFP imaging at 3Tesla.” Magnetic Resonance in Medicine: An Official Journal of theInternational Society for Magnetic Resonance in Medicine 51.4 (2004):799-806.—discloses a sequence protocol for cardiac SSFP imaging at 3.0T,taking into account several partly adverse effects at higher field, suchas increased field inhomogeneities, longer T1, and power depositionlimitations.

Non-patent literature—Deshpande, Vibhas S., Steven M. Shea, and DebiaoLi. “Artifact reduction in true-FISP imaging of the coronary arteries byadjusting imaging frequency.” Magnetic Resonance in Medicine: AnOfficial Journal of the International Society for Magnetic Resonance inMedicine 49.5 (2003): 803-809.—discloses a scouting method forestimating an optimal synthesizer frequency for a volume of interest anda similar scouting method for determining the optimal frequency offsetfor fat saturation pulse.

Coronary artery imaging was performed in healthy subjects using a 3Dfast imaging with steady-state precession sequence to validate theeffectiveness of the frequency corrections. Substantial reduction inimage artifacts and improvement in fat suppression were observed byusing the water and fat frequencies estimated by the scouting scans.

In existing selection procedure of the frequency offset, the medicalexpert has to check frame-by-frame where the artifacts occur withrespect to the ROI. This process needs expertise, is time-consuming andtherefore expensive as well as also prone to errors.

SUMMARY

Therefore, a need exists for advanced techniques of performing MRI, inparticular, MRI using SSFP or bSSFP pulse sequences. More specifically,a need exists for techniques of reducing artifacts, e.g., bandingartifacts and/or flow-related artifacts, when performing MRI.

At least this need is met by the features of embodiments describedherein and the independent claims. The features of the dependent claimsfurther define embodiments.

According to examples disclosed herein, methods, computing devices, andMRI scanners for selecting a frequency offset for an MRI scan aredisclosed. Such methods facilitate automatically determining/selectingan optimal frequency offset for an MRI scan following a frequency scoutscan, in particular, for an SSFP or a bSSFP pulse sequence. By using thedetermined/selected optimal frequency offset, banding artifacts and/orflow-related artifacts can be reduced.

A computer-implemented method is provided. The method comprisesobtaining multiple MRI images. The multiple MRI images are obtained fromfrequency scout measurements associated with multiple frequency offsetsfrom a reference frequency of a magnetization excitation pulse. Themethod further comprises determining from the multiple MRI images, atleast one reference image based on spectrum information of each of themultiple MRI images. The method still further comprises determining, foreach one of the multiple MRI images, a respective similarity measurebased on a comparison between the at least one reference image and therespective MRI image, and selecting, based on the similarity measures, afrequency offset for a subsequent MRI scan from the multiple frequencyoffsets.

A computer program or a computer-program product or a computer-readablestorage medium that includes program code is provided. The program codecan be loaded and executed by at least one processor. Upon loading andexecuting the program code, the at least one processor performs amethod. The method comprises obtaining multiple MRI images. The multipleMRI images are obtained from frequency scout measurements associatedwith multiple frequency offsets from a reference frequency of amagnetization excitation pulse. The method further comprises determiningfrom the multiple MRI images, at least one reference image based onspectrum information of each of the multiple MRI images. The methodstill further comprises determining, for each one of the multiple MRIimages, a respective similarity measure based on a comparison betweenthe at least one reference image and the respective MRI image, andselecting, based on the similarity measures, a frequency offset for asubsequent MRI scan from the multiple frequency offsets.

A computing device is provided. The computing device comprises aprocessor and a memory. The processor is configured to load program codefrom the memory and execute the program code. Upon executing the programcode, the processor is configured to obtain multiple MRI images. Themultiple MRI images are obtained from frequency scout measurementsassociated with multiple frequency offsets from a reference frequency ofa magnetization excitation pulse. The processor is further configured todetermine from the multiple MRI images, at least one reference imagebased on spectrum information of each of the multiple MRI images. Theprocessor is still further configured to determine, for each one of themultiple MRI images, a respective similarity measure based on acomparison between the at least one reference image and the respectiveMRI image, and select, based on the similarity measures, a frequencyoffset for a subsequent MRI scan from the multiple frequency offsets.

An MRI scanner is provided. The MRI scanner comprises the computingdevice described above.

It is to be understood that the features mentioned above and those yetto be explained below may be used not only in the respectivecombinations indicated, but also in other combinations or in isolationwithout departing from the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an MRI scanner according to variousexamples.

FIG. 2 schematically illustrates a flowchart of a method according tovarious examples.

FIG. 3 schematically illustrates a block diagram of a computing deviceaccording to various examples.

DETAILED DESCRIPTION

Some examples of the present disclosure generally provide for aplurality of circuits or other electrical devices. All references to thecircuits and other electrical devices and the functionality provided byeach are not intended to be limited to encompassing only what isillustrated and described herein. While particular labels may beassigned to the various circuits or other electrical devices disclosed,such labels are not intended to limit the scope of operation for thecircuits and the other electrical devices. Such circuits and otherelectrical devices may be combined with each other and/or separated inany manner based on the particular type of electrical implementationthat is desired. It is recognized that any circuit or other electricaldevice disclosed herein may include any number of microcontrollers, agraphics processor unit (GPU), integrated circuits, memory devices(e.g., FLASH, random access memory (RAM), read only memory (ROM),electrically programmable read only memory (EPROM), electricallyerasable programmable read only memory (EEPROM), or other suitablevariants thereof), and software which co-act with one another to performoperation(s) disclosed herein. In addition, any one or more of theelectrical devices may be configured to execute a program code that isembodied in a non-transitory computer readable medium programmed toperform any number of the functions as disclosed.

In the following, embodiments of the present invention will be describedin detail with reference to the accompanying drawings. It is to beunderstood that the following description of embodiments is not to betaken in a limiting sense. The scope of the present invention is notintended to be limited by the embodiments described hereinafter or bythe drawings, which are taken to be illustrative only.

The drawings are to be regarded as being schematic representations andelements illustrated in the drawings are not necessarily shown to scale.Rather, the various elements are represented such that their functionand general purpose become apparent to a person skilled in the art. Anyconnection or coupling between functional blocks, devices, components,or other physical or functional units shown in the drawings or describedherein may also be implemented by an indirect connection or coupling. Acoupling between components may also be established over a wirelessconnection. Functional blocks may be implemented in hardware, firmware,software, or a combination thereof.

Hereinafter, techniques of MRI are described. MRI may be employed toobtain raw MR signals of a magnetization of nuclear spins of a sampleregion of the patient (MRI data). The sample region defines a field ofview (FOV). The FOV typically includes a smaller region of interest(ROI) and surrounding tissue or background. The MRI data are defined ink-space. Based on the MRI data, MRI images in either k-space or spatialdomain can be determined.

As a general rule, the term MRI image used herein denotes a 2-D or 3-Dspatial dataset. This can be obtained, e.g., from reconstruction of MRdata using techniques known to the skilled person.

According to various examples, SSFP or bSSFP pulse sequences may beemployed for acquiring MRI data. The bSSFP pulse sequence has become amajor imaging method in clinical practice, such as cardiovascularmagnetic resonance because of advantages of sub second scan time, highfluid-tissue contrast, and three-dimensional imaging compatibility.

In general, SSFP MRI sequences are based on a low flip angle gradientecho MRI sequence with a short repetition time, to provide timeresolution. The repetition time—typically fixed—can be smaller than thetransverse relaxation time of the magnetization. I.e., a spin echo of anext RF pulse can be located within the free-induction decay of themagnetization of a preceding RF pulse. Thus, a sequence of RF pulses isused. This generates a signal of time-varying amplitude. Differentmagnetization steady states can be generated, depending on the usedgradient fields. bSSFP MRI sequences are a specific form of SSFP MRIsequences, where each gradient pulse within a repetition time (TR) iscompensated with another gradient pulse of opposite polarity to avoidany dephasing due to gradient switching. Details of the SSFP MRIsequences are disclosed in Bieri, Oliver, and Klaus Scheffler.“Fundamentals of balanced steady state free precession MRI.” Journal ofMagnetic Resonance Imaging 38.1 (2013): 2-11.

To facilitate the utilization of SSFP pulse sequences, frequency scoutmeasurements can be used to identify the optimum frequency offsetassociated with the SSFP pulse sequence, which may minimize the presenceof artifacts in the MRI images, e.g., artifacts arising because ofglobal heterogeneity of the main magnetic field. The frequency scout isa discrete search approach. It acquires SSFP images with varyingfrequency offsets of the excitation pulse with respect to a referencefrequency.

According to this disclosure, during a frequency scout scan, frequencyscout measurements associated with multiple frequency offsets from areference frequency of a magnetization excitation pulse, e.g., theLarmor frequency, is obtained, and thereby multiple MRI images areobtained from the frequency scout measurements. At least one referenceimage is determined from the multiple MRI images based on spectruminformation of each of the multiple MRI images, and a respectivesimilarity measure is determined based on a comparison between the atleast one reference image and each one of the multiple MRI images. Then,a frequency offset for a subsequent MRI scan from the multiple frequencyoffsets is selected based on the similarity measures. According to thedisclosure, the MRI scan may then be executed using the selectedfrequency offset.

By selecting the frequency offset for a subsequent MRI scan from themultiple frequency offsets based on the similarity measures whichrespectively characterize how similar each one of the multiple MRIimages is to the at least one reference image, the optimal offset for asubsequent MRI scan can be selected. Such a selection can beautomatically performed using a computing device, such as a personalcomputer, a server, a workstation, etc. Thus, the number of frequencyoffsets per hertz (Hz) used in the frequency scout measurement may beincreased, and thereby the optimal frequency offset can be determinedmore precisely, reliably, and efficiently compared to being manuallyselected by a medical expert. Human error may be reduced.

FIG. 1 illustrates aspects with respect to an MRI scanner 100. The MRIscanner 100 can be used to perform the frequency scout measurement andthe subsequent MRI scan using the frequency offset determined/selectedbased on the frequency scout measurement. There are several principalcomponents constituting an MRI scanner 100: the main magnet 110; a setof gradient coils 120 to provide switchable spatial gradients in themain magnetic field; radio frequency (RF) coils 130 (or resonators) forthe transmission and reception of radio frequency pulses; pulse sequenceelectronics 140 for programming the timing of transmission signals(excitation pulse, gradient signals); image reconstruction electronics150 and a human machine interface 160 for viewing, manipulating, andstoring images.

A common type of the main magnet 110 used in MRI systems is thecylindrical superconducting magnet (typically with a 1 meter bore size).The main magnet 110 can provide a main magnet field with a fieldstrength varying from 0.5 T (21 MHz) to 3.0 T (128 MHz), even 9 T (383MHz), along its longitudinal axis. The main magnetic field can align themagnetization of the nuclear spins of a patient along the longitudinalaxis. The patient can be moved into the bore by a sliding table (notshown in FIG. 1 ).

Typically, the main magnetic field exhibits inhomogeneities, i.e., localdeviations from the nominal value. These are due to magnetic materials,non-perfect coils, etc. The inhomogeneities can lead to artifacts,specifically in SSFP measurements. Techniques are disclosed thatfacilitate reducing such artifacts.

The gradient coils 120 fit inside the bore of the main magnet 110 (afterany active shimming coils, if present). The function of the gradientcoils 120 is to provide a temporary change in the magnitude of the mainmagnetic field as a function of position in the bore of the main magnet110. The gradient coils 120 provide a spatial encoding of the magneticfield strength, to thereby choose slices of the patient body forselective imaging. In this way, MRI can be tomographic—i.e., it canimage slices. The gradient coils 120 also provide the mechanism and/ormeans to spatially encode the voxels within a given image slice so thatthe individual echoes coming from each voxel can be discriminated andturned into an MR image. There are usually three orthogonal gradientcoils, one for each of the physical x, y, and z directions. Thegradients can be used for slice selection (slice-selection gradients),frequency encoding (readout gradients), and phase encoding along one ormore phase-encoding directions (phase-encoding gradients). Hereinafter,the slice-selection direction will be defined as being aligned along theZ-axis; the readout direction will be defined as being aligned with theX-axis; and a first phase-encoding direction as being aligned with theY-axis. A second phase-encoding direction may be aligned with theZ-axis. The directions along which the various gradients are applied arenot necessarily in parallel with the axes defined by the gradient coils120. Rather, it is possible that these directions are defined by acertain k-space trajectory which, in turn, can be defined by certainrequirements of the respective MRI pulse sequence and/or based onanatomic properties of a patient. The gradient coils 120 usually coupledwith the pulse sequence electronics 140 via gradient amplifiers 170.

RF pulses that are oscillating at the Larmor frequency (or Larmorfrequency+frequency offset) applied around a sample causes nuclear spinsto precess, tipping them toward the transverse plane. Once a spin systemis excited, coherently rotating spins can induce RF currents (at theLarmor frequency or Larmor frequency+frequency offset) in nearbyantennas, yielding measurable signals associated with the free inductiondecay and echoes. Thus, the RF coils 130 serve to both induce spinprecession and to detect signals indicative of the precession of thenuclear spins. The RF coils 130 usually coupled with both the pulsesequence electronics 140 and the image reconstruction electronics 150via RF electronics 180, respectively.

For creating such RF pulses, an RF transmitter (e.g., a part of the RFelectronics 180) is connected via an RF switch (e.g., a part of the RFelectronics 180) with the RF coils 130. Via an RF receiver (e.g., a partof the RF electronics 180), it is possible to detect the inducedcurrents or signals by the spin system. In particular, it is possible todetect echoes; echoes may be formed by applying one or more RF pulses(spin echo) and/or by applying one or more gradients (gradient echo).The respectively induced currents or signals can correspond to raw MRIdata in k-space; according to various examples, the MRI data can beprocessed using reconstruction techniques in order to obtain MRI images.

The human machine interface 160 might include at least one of a screen,a keyboard, a mouse, etc. Via the human machine interface 160, a userinput can be detected and output to the user can be implemented. Forexample, via the human machine interface 160, it is possible to selectand configure the scanning pulse sequence, e.g., an SSFP pulse sequence,graphically select the orientation of the scan planes to image, reviewimages obtained, and change variables in the pulse sequence to modifythe contrast between tissues. The human machine interface 160 isrespectively connected to the pulse sequence electronics 140 and theimage reconstruction electronics 150, such as an array processor, whichperforms the image reconstruction.

The pulse sequence electronics 140 may include a GPU and/or a CPU and/oran application-specific integrated circuit and/or a field-programmablearray. The pulse sequence electronics 140 may implement various controlfunctionality with respect to the operation of the MRI scanner 100, e.g.based on program code loaded from a memory. For example, the pulsesequence electronics 140 could implement a sequence control fortime-synchronized operation of the gradient coils 120, both the RFtransmitter and the RF receiver of the RF electronics 180.

The image reconstruction electronics 150 may include a GPU and/or a CPUand/or an application-specific integrated circuit and/or afield-programmable array. The image reconstruction electronics 150 canbe configured to implement post-processing for reconstruction of MRIimages.

The pulse sequence electronics 140 and the image reconstructionelectronics 150 may be a single circuit, or two separate circuits.

The MRI scanner 100 may be connectable to a database (not shown in FIG.1 ), such as a picture archiving and communication system (PACS) locatedwithin a local network of a hospital, for storing acquired MRI data,and/or, MRI images in k-space, and reconstructed MRI images in spatialdomain.

According to this disclosure, the pulse sequence electronics 140 of theMRI scanner 100 can be configured/programmed according to techniquesdisclosed hereinafter to control the RF coils 130 via the RF electronics180 to generate a magnetization excitation pulse with an optimalfrequency, e.g., the Larmor frequency+an optimal frequency offset.Therefore, artifacts, particularly due to inhomogeneity of the mainmagnetic field can be precisely reduced.

Details with respect to techniques, such as the functioning of the MRIscanner 100, particularly the functioning of the pulse sequenceelectronics 140 and/or the image reconstruction electronics 150 aredescribed in connection with FIG. 2 .

FIG. 2 is a flowchart of a method 1000 according to various examples.For example, the method 1000 according to FIG. 2 may be executed byeither the image reconstruction electronics 150 or the pulse sequenceelectronics 140 of the MRI scanner 100 according to the example of FIG.1 , e.g., upon loading program code from a memory. The method 1000 maybe executed by the image reconstruction electronics 150 together withthe pulse sequence electronics 140, for example, the imagereconstruction electronics 150 may be configured to process MRI imagesand the pulse sequence electronics 140 may be configured to generate anacquisition protocol, e.g., the SSFP pulse sequences. It would also bepossible that the method 1000 is at least partially executed by aseparate compute unit, e.g., at a server backend.

FIG. 2 illustrates aspects with respect to selecting, based on frequencyscout measurements obtained during a frequency scout scan, a frequencyoffset from a reference frequency of a magnetization excitation pulsefor a subsequent MRI scan. During the frequency scout scan, thefrequency scout measurements associated with multiple frequency offsetsfrom a reference frequency of a magnetization excitation pulse, e.g.,the Larmor frequency, are obtained, and thereby multiple MRI images,e.g., either in k-space or spatial domain, are obtained from thefrequency scout measurements.

Each MRI image is associated with a respective frequency offset.Different MRI images show smaller or larger artifacts in a ROI, due tothe different frequency offsets. To determine which MRI images showlarger or smaller artifacts in the ROI at least one reference image isdetermined from the multiple MRI images based on spectrum information ofeach of the multiple MRI images, and a respective similarity measure isdetermined based on a comparison between the at least one referenceimage and each one of the multiple MRI images. Then, a frequency offsetfor a subsequent MRI scan from the multiple frequency offsets isselected based on the similarity measures. Details of the method 1000are described below.

At block 1010, multiple MRI images are obtained and the multiple MRIimages are obtained from frequency scout measurements associated withmultiple frequency offsets from a reference frequency of a magnetizationexcitation pulse.

For example, the multiple MRI images may be obtained from the MRIscanner of FIG. 1 or a database, such as a PACS. The multiple MRI imagesmay be obtained from a frequency scout scan using the same SSFP pulsesequence with varying frequency offsets of a magnetization excitationpulse, e.g., ±0.5 Hz, ±1.0 Hz, ±1.5 Hz, . . . , from a 128 MHzmagnetization excitation pulse. The total number of the frequencyoffsets used in the frequency scout measurement may be 20-50, 200-500,or even more than 1,000. There is a tendency that the higher the numberof frequency offsets, the more precise the optimal frequency offset.

At block 1020, at least one reference image is determined from themultiple MRI images based on spectrum information of each of themultiple MRI images.

Depending on the scenario, a single reference image or two or morereference images may be determined.

The spectrum information may pertain, as a general rule, to spatialfrequency components of the MRI images. According to this disclosure,the spectrum information may represent how quickly/slowly the pixels ofan MRI image change in contrast in both the x and y spatial dimensions.If the multiple MRI images obtained at block 1010 are k-space images,the spectrum information can be obtained from the multiple MRI imagesthemselves, i.e., information comprised in k-space. If the multiple MRIimages obtained at block 1010 are in image domain, e.g., x-y domain, thespectrum information can be obtained by applying the Fouriertransformation to each one of the multiple MRI images, i.e., k-spaceimages.

According to various examples, the spectrum information of each of themultiple MRI images may comprise a high-frequency signal contributionwhich may be determined based on applying a high-pass filter to each ofthe multiple MRI images. Alternatively or additionally, the spectruminformation of each of the multiple MRI images may be determined basedon applying a low-pass filter to each of the multiple MRI images. Such ahigh-pass filter or a low-pass filter may be those defined associatedwith conventional programming languages, e.g., C++, Python, or Matlab.The cutoff frequency of either the high-frequency filter or the low-passfilter may be the same.

Specifically, according to the disclosure, the spectrum information mayquantify a high-frequency signal contribution. I.e., a high pass filtermay be applied to the k-space representation of each MRI image (e.g.,selectively in the ROI), and the remaining post-filter pixel values canbe summed.

The high-frequency signal contribution could be determined as follows:an MRI image is Fourier transformed to k-space. Then, the total signalat high spatial frequencies beyond a predetermined threshold can bedetermined by integration of the respective image values in k-space, forone or two of the k-space directions.

The method may include determining, from the multiple MRI images, asubset including two or more MRI images based on the spectruminformation of each of the multiple MRI images, and the at least onereference image is determined based on the subset of the two or more MRIimages. For example, the two or three images respectively having thesmallest high-frequency components (e.g., specifically in the ROI) ifcompared to the other MRI images can be included in the subset.

Then, the at least one reference image is determined based on thesubset.

For instance, where two or three or more MRI images are included in thesubset, a single reference image may be determined based on a pixel-wiseaverage of the MRI images in the subset. Another combination would bepossible, e.g., weighted average wherein the weighting factors arederived from the high-frequency components.

According to this disclosure, the two or more MRI images included in thesubset may have a minimum high-frequency signal contribution.

In another scenario, all MRI images in the subset may be retained asreference images (then yielding more than a single reference image).

At block 1030, a respective similarity measure is determined for eachMRI image based on a comparison between the at least one reference imageand each one of the multiple MRI images.

For example, the similarity measures may be determined based on thedisclosure related to structural similarity of non-patentliterature—Wang, Zhou, et al. “Image quality assessment: from errorvisibility to structural similarity.” IEEE transactions on imageprocessing 13.4 (2004): 600-612.

For example, the similarity measures can be determined using a trainedmachine learning algorithm, for example, the Deep Learning-guidedAdaptive Weighted Averaging algorithm disclosed in a non-patentliterature—Gadjimuradov, Fasil, et al. “Deep learning-guided weightedaveraging for signal dropout compensation in diffusion-weighted imagingof the liver.” arXiv preprint arXiv:2202.09912 (2022).

In one scenario, the trained machine learning algorithm can determine asimilarity score for each pair of input images, i.e., a given referenceimage and a given MRI image. For instance, respective training data maybe generated manually by an expert, assigning respective similarityscores as ground truth.

For instance, the trained machine learning algorithm may generate arespective weighting map for each one of the multiple MRI images, andthe respective weighting map may penalize signal deviations from apixel-wise median that is calculated based on the at least one referenceimage, and the respective similarity measure may be determined based onthe respective weighting map.

If a single reference image is determined at block 1020, then asimilarity measure is determined between each one of the MRI images andthe single reference image.

If multiple reference images are determined at block 1020, for example,three reference images are determined from twelve MRI images, i.e.,twelve MRI images are obtained at block 1010, then based on each of thethree reference images, a respective partial similarity measure may bedetermined for each of the twelve MRI images. In total, 36 partialsimilarity measures are obtained. Then, to yield the (final) similaritymeasure for a given MRI image, the partial similarity measures can becombined, e.g., average, for each MRI image.

At block 1040, a frequency offset for a subsequent MRI scan is selectedfrom the multiple frequency offsets based on the similarity measures.

For example, the selecting of the frequency offset for the subsequentMRI scan may comprise selecting a given MRI image from the multiple MRIimages which is associated with the maximum similarity measure, and thegiven MRI image is acquired in the frequency scout measurement using theselected frequency offset. For example, during the frequency scout scan,100 frequency offsets are used, and the MRI image obtained using thefrequency offset 26 Hz has the maximum similarity measure according toblock 1030. Thus, the frequency offset 26 Hz is selected as thefrequency offset for performing the subsequent MRI scan.

According to various examples, artifacts occurring in a specificposition of the multiple MRI images may be relevant for clinicalpractice, e.g., in a part of the multiple MRI images which respectivelydepicts a heart of a patient. Thus, to further facilitate a more preciseselection of the frequency offset for the subsequent MRI scan, themethod 1000 may further comprise determining a ROI of each of themultiple MRI images, and the spectrum information of each of themultiple MRI images is associated with the ROI. The spectrum informationmay be determined only based on information included in the ROI or theROI may be weighted higher than the surrounding.

For example, the ROI may be determined based on segmentation techniquesaccording to either classical computer vision approaches orartificial-intelligence-based (AI-based) approaches, e.g., a trainedsegmentation machine learning algorithm for segmenting the ROI from atleast one of the multiple MRI images. In general, the multiple MRIimages obtained from frequency scout measurements associated withmultiple frequency offsets depict the same anatomy, and thereby the ROIin each of the multiple MRI images may be the same. Thus, as far as theROI in a single MRI image is determined/segmented, the respective ROI inthe other MRI images may be determined, e.g., by applying the samespatial position, e.g., coordinate pairs in the x-y domain/plane, of thedetermined ROI in the single MRI image. Alternatively, it is alsopossible to determine the ROI from each of the multiple MRI images,e.g., using the same segmentation techniques.

According to various examples, the similarity measures may be determinedbased on comparisons that are restricted to the ROI. For instance, for agiven reference image and a given MRI image, the similarity measure ofthe given MRI image is determined based on a comparison between the ROIof the reference image and the ROI of the given MRI image. Thesurrounding of the ROI may be neglected.

By considering the ROI, it can be ensured that artifacts do not occur inthe clinically-relevant regions. Artifacts can be pushed to other, lessrelevant regions in the surrounding of the ROI.

According to various examples, the subsequent MRI scan may be based onan SSFP pulse sequence or a bSSFP pulse sequence using the determinedfrequency offset. Thus, the method 1000 may further comprise configuringthe MRI scanner 100 of FIG. 1 , e.g., the pulse sequence electronics140, to generate the SSFP pulse sequence or the bSSFP pulse sequenceusing the determined frequency offset.

According to this disclosure, the method 1000 may facilitate performinga subsequent MRI scan which comprises at least one of a cardiac imagingscan, a fetal imaging scan, and an abdominal imaging scan.

According to the method 1000, the frequency offset for a subsequent MRIscan may be selected from the multiple frequency offsets based on thesimilarity measures which respectively characterize how similar each oneof the multiple MRI images is to the at least one reference image, andthereby the optimal offset for a subsequent MRI scan can be selected.Such a selection can be automatically performed using a computingdevice, such as the image reconstruction electronics 150 and/or thepulse sequence electronics 140 of the MRI scanner of FIG. 1 , or apersonal computer, a server, or a workstation. Thus, the number offrequency offsets per hertz (Hz) used in the frequency scout measurementcan be increased greatly, and thereby the optimal frequency offset canbe determined more precisely, reliably, and efficiently compared tobeing manually selected by a medical expert. Further, the waiting timefor a patient in between the scout scan and the subsequent MRI scan maybe considerably reduced and thereby patient experience can be improved.

FIG. 3 is a block diagram of a computing device 9000 according tovarious examples. The computing device 9000 may comprise a processor9020, a memory 9030, and an input/output interface 9010. The processor9020 is configured to load program code from the memory 9030 and executethe program code. Upon executing the program code, the processor 9020performs the method 1000 for determining/selecting a frequency offsetfor a subsequent MRI scan.

Referring to FIG. 1 again, the MRI scanner 100 may further comprise thecomputing device 9000 configured to perform the method 1000. Thecomputing device 9000 may be the image reconstruction electronics 150and/or the pulse sequence electronics 140. I.e., the computing device9000 may be embedded in or connected with the MRI scanner 100, andthereby the MRI scanner 100 may be also configured to perform the method1000.

Summarizing, techniques have been described that facilitateautomatically determining/selecting an optimal frequency offset for asubsequent MRI scan, in particular, for an SSFP or a bSSFP pulsesequence. By using the determined/selected optimal frequency offset,banding artifacts and/or flow-related artifacts can be reduced. Further,by using the automatically determining/selecting techniques, the numberof frequency offsets per hertz (Hz) used in the frequency scout scan canbe increased greatly, and thereby the optimal frequency offset can bedetermined more precisely, reliably, and efficiently compared to beingmanually selected by a medical expert. Further, the waiting time for apatient in between the scout scan and the subsequent MRI scan may beconsiderably reduced and thereby patient experience can be improved. Inaddition, the optimal frequency offset may be determined for a specificROI but not the whole MRI image, which may further facilitate thereduction of artifacts occurred in a specific ROI.

Although the disclosure has been shown and described with respect tocertain preferred embodiments, equivalents and modifications will occurto others skilled in the art upon the reading and understanding of thespecification. The present disclosure includes all such equivalents andmodifications and is limited only by the scope of the appended claims.

For illustration, an image domain similarity measure between the atleast one reference image and the MRI images has been disclosed. Itwould also be possible that the similarity measure is determined atleast partly based on spectrum information.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions,layers, and/or sections, these elements, components, regions, layers,and/or sections, should not be limited by these terms. These terms areonly used to distinguish one element from another. For example, a firstelement could be termed a second element, and, similarly, a secondelement could be termed a first element, without departing from thescope of example embodiments. As used herein, the term “and/or,”includes any and all combinations of one or more of the associatedlisted items. The phrase “at least one of” has the same meaning as“and/or”.

Spatially relative terms, such as “beneath,” “below,” “lower,” “under,”“above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. It will beunderstood that the spatially relative terms are intended to encompassdifferent orientations of the device in use or operation in addition tothe orientation depicted in the figures. For example, if the device inthe figures is turned over, elements described as “below,” “beneath,” or“under,” other elements or features would then be oriented “above” theother elements or features. Thus, the example terms “below” and “under”may encompass both an orientation of above and below. The device may beotherwise oriented (rotated 90 degrees or at other orientations) and thespatially relative descriptors used herein interpreted accordingly. Inaddition, when an element is referred to as being “between” twoelements, the element may be the only element between the two elements,or one or more other intervening elements may be present.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including “on,”“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Incontrast, when an element is referred to as being “directly” on,connected, engaged, interfaced, or coupled to another element, there areno intervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between,” versus “directly between,” “adjacent,” versus“directly adjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a,” “an,” and “the,”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. As used herein, the terms “and/or” and “atleast one of” include any and all combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including,” when usedherein, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof. As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items. Expressions such as “at least one of,” whenpreceding a list of elements, modify the entire list of elements and donot modify the individual elements of the list. Also, the term “example”is intended to refer to an example or illustration.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belong. Itwill be further understood that terms, e.g., those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

It is noted that some example embodiments may be described withreference to acts and symbolic representations of operations (e.g., inthe form of flow charts, flow diagrams, data flow diagrams, structurediagrams, block diagrams, etc.) that may be implemented in conjunctionwith units and/or devices discussed above. Although discussed in aparticularly manner, a function or operation specified in a specificblock may be performed differently from the flow specified in aflowchart, flow diagram, etc. For example, functions or operationsillustrated as being performed serially in two consecutive blocks mayactually be performed simultaneously, or in some cases be performed inreverse order. Although the flowcharts describe the operations assequential processes, many of the operations may be performed inparallel, concurrently or simultaneously. In addition, the order ofoperations may be re-arranged. The processes may be terminated whentheir operations are completed, but may also have additional steps notincluded in the figure. The processes may correspond to methods,functions, procedures, subroutines, subprograms, etc.

Specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Thepresent invention may, however, be embodied in many alternate forms andshould not be construed as limited to only the embodiments set forthherein.

In addition, or alternative, to that discussed above, units and/ordevices according to one or more example embodiments may be implementedusing hardware, software, and/or a combination thereof. For example,hardware devices may be implemented using processing circuity such as,but not limited to, a processor, Central Processing Unit (CPU), acontroller, an arithmetic logic unit (ALU), a digital signal processor,a microcomputer, a field programmable gate array (FPGA), aSystem-on-Chip (SoC), a programmable logic unit, a microprocessor, orany other device capable of responding to and executing instructions ina defined manner. Portions of the example embodiments and correspondingdetailed description may be presented in terms of software, oralgorithms and symbolic representations of operation on data bits withina computer memory. These descriptions and representations are the onesby which those of ordinary skill in the art effectively convey thesubstance of their work to others of ordinary skill in the art. Analgorithm, as the term is used here, and as it is used generally, isconceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of optical, electrical, or magnetic signals capable of beingstored, transferred, combined, compared, and otherwise manipulated. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

It should be borne in mind that all of these and similar terms are to beassociated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. Unless specificallystated otherwise, or as is apparent from the discussion, terms such as“processing” or “computing” or “calculating” or “determining” of“displaying” or the like, refer to the action and processes of acomputer system, or similar electronic computing device/hardware, thatmanipulates and transforms data represented as physical, electronicquantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

In this application, including the definitions below, the term ‘module’or the term ‘controller’ may be replaced with the term ‘circuit.’ Theterm ‘module’ may refer to, be part of, or include processor hardware(shared, dedicated, or group) that executes code and memory hardware(shared, dedicated, or group) that stores code executed by the processorhardware.

The module may include one or more interface circuits. In some examples,the interface circuits may include wired or wireless interfaces that areconnected to a local area network (LAN), the Internet, a wide areanetwork (WAN), or combinations thereof. The functionality of any givenmodule of the present disclosure may be distributed among multiplemodules that are connected via interface circuits. For example, multiplemodules may allow load balancing. In a further example, a server (alsoknown as remote, or cloud) module may accomplish some functionality onbehalf of a client module.

Software may include a computer program, program code, instructions, orsome combination thereof, for independently or collectively instructingor configuring a hardware device to operate as desired. The computerprogram and/or program code may include program or computer-readableinstructions, software components, software modules, data files, datastructures, and/or the like, capable of being implemented by one or morehardware devices, such as one or more of the hardware devices mentionedabove. Examples of program code include both machine code produced by acompiler and higher level program code that is executed using aninterpreter.

For example, when a hardware device is a computer processing device(e.g., a processor, Central Processing Unit (CPU), a controller, anarithmetic logic unit (ALU), a digital signal processor, amicrocomputer, a microprocessor, etc.), the computer processing devicemay be configured to carry out program code by performing arithmetical,logical, and input/output operations, according to the program code.Once the program code is loaded into a computer processing device, thecomputer processing device may be programmed to perform the programcode, thereby transforming the computer processing device into a specialpurpose computer processing device. In a more specific example, when theprogram code is loaded into a processor, the processor becomesprogrammed to perform the program code and operations correspondingthereto, thereby transforming the processor into a special purposeprocessor.

Software and/or data may be embodied permanently or temporarily in anytype of machine, component, physical or virtual equipment, or computerstorage medium or device, capable of providing instructions or data to,or being interpreted by, a hardware device. The software also may bedistributed over network coupled computer systems so that the softwareis stored and executed in a distributed fashion. In particular, forexample, software and data may be stored by one or more computerreadable recording mediums, including the tangible or non-transitorycomputer-readable storage media discussed herein.

Even further, any of the disclosed methods may be embodied in the formof a program or software. The program or software may be stored on anon-transitory computer readable medium and is adapted to perform anyone of the aforementioned methods when run on a computer device (adevice including a processor). Thus, the non-transitory, tangiblecomputer readable medium, is adapted to store information and is adaptedto interact with a data processing facility or computer device toexecute the program of any of the above mentioned embodiments and/or toperform the method of any of the above mentioned embodiments.

Example embodiments may be described with reference to acts and symbolicrepresentations of operations (e.g., in the form of flow charts, flowdiagrams, data flow diagrams, structure diagrams, block diagrams, etc.)that may be implemented in conjunction with units and/or devicesdiscussed in more detail below. Although discussed in a particularlymanner, a function or operation specified in a specific block may beperformed differently from the flow specified in a flowchart, flowdiagram, etc. For example, functions or operations illustrated as beingperformed serially in two consecutive blocks may actually be performedsimultaneously, or in some cases be performed in reverse order.

According to one or more example embodiments, computer processingdevices may be described as including various functional units thatperform various operations and/or functions to increase the clarity ofthe description. However, computer processing devices are not intendedto be limited to these functional units. For example, in one or moreexample embodiments, the various operations and/or functions of thefunctional units may be performed by other ones of the functional units.Further, the computer processing devices may perform the operationsand/or functions of the various functional units without sub-dividingthe operations and/or functions of the computer processing units intothese various functional units.

Units and/or devices according to one or more example embodiments mayalso include one or more storage devices. The one or more storagedevices may be tangible or non-transitory computer-readable storagemedia, such as random access memory (RAM), read only memory (ROM), apermanent mass storage device (such as a disk drive), solid state (e.g.,NAND flash) device, and/or any other like data storage mechanism capableof storing and recording data. The one or more storage devices may beconfigured to store computer programs, program code, instructions, orsome combination thereof, for one or more operating systems and/or forimplementing the example embodiments described herein. The computerprograms, program code, instructions, or some combination thereof, mayalso be loaded from a separate computer readable storage medium into theone or more storage devices and/or one or more computer processingdevices using a drive mechanism. Such separate computer readable storagemedium may include a Universal Serial Bus (USB) flash drive, a memorystick, a Blu-ray/DVD/CD-ROM drive, a memory card, and/or other likecomputer readable storage media. The computer programs, program code,instructions, or some combination thereof, may be loaded into the one ormore storage devices and/or the one or more computer processing devicesfrom a remote data storage device via a network interface, rather thanvia a local computer readable storage medium. Additionally, the computerprograms, program code, instructions, or some combination thereof, maybe loaded into the one or more storage devices and/or the one or moreprocessors from a remote computing system that is configured to transferand/or distribute the computer programs, program code, instructions, orsome combination thereof, over a network. The remote computing systemmay transfer and/or distribute the computer programs, program code,instructions, or some combination thereof, via a wired interface, an airinterface, and/or any other like medium.

The one or more hardware devices, the one or more storage devices,and/or the computer programs, program code, instructions, or somecombination thereof, may be specially designed and constructed for thepurposes of the example embodiments, or they may be known devices thatare altered and/or modified for the purposes of example embodiments.

A hardware device, such as a computer processing device, may run anoperating system (OS) and one or more software applications that run onthe OS. The computer processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For simplicity, one or more example embodiments may beexemplified as a computer processing device or processor; however, oneskilled in the art will appreciate that a hardware device may includemultiple processing elements or processors and multiple types ofprocessing elements or processors. For example, a hardware device mayinclude multiple processors or a processor and a controller. Inaddition, other processing configurations are possible, such as parallelprocessors.

The computer programs include processor-executable instructions that arestored on at least one non-transitory computer-readable medium (memory).The computer programs may also include or rely on stored data. Thecomputer programs may encompass a basic input/output system (BIOS) thatinteracts with hardware of the special purpose computer, device driversthat interact with particular devices of the special purpose computer,one or more operating systems, user applications, background services,background applications, etc. As such, the one or more processors may beconfigured to execute the processor executable instructions.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language) or XML (extensible markuplanguage), (ii) assembly code, (iii) object code generated from sourcecode by a compiler, (iv) source code for execution by an interpreter,(v) source code for compilation and execution by a just-in-timecompiler, etc. As examples only, source code may be written using syntaxfrom languages including C, C++, C#, Objective-C, Haskell, Go, SQL, R,Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5,Ada, ASP (active server pages), PHP, Scala, Eiffel, Smalltalk, Erlang,Ruby, Flash®, Visual Basic®, Lua, and Python®.

Further, at least one example embodiment relates to the non-transitorycomputer-readable storage medium including electronically readablecontrol information (processor executable instructions) stored thereon,configured in such that when the storage medium is used in a controllerof a device, at least one embodiment of the method may be carried out.

The computer readable medium or storage medium may be a built-in mediuminstalled inside a computer device main body or a removable mediumarranged so that it can be separated from the computer device main body.The term computer-readable medium, as used herein, does not encompasstransitory electrical or electromagnetic signals propagating through amedium (such as on a carrier wave); the term computer-readable medium istherefore considered tangible and non-transitory. Non-limiting examplesof the non-transitory computer-readable medium include, but are notlimited to, rewriteable non-volatile memory devices (including, forexample flash memory devices, erasable programmable read-only memorydevices, or a mask read-only memory devices); volatile memory devices(including, for example static random access memory devices or a dynamicrandom access memory devices); magnetic storage media (including, forexample an analog or digital magnetic tape or a hard disk drive); andoptical storage media (including, for example a CD, a DVD, or a Blu-rayDisc). Examples of the media with a built-in rewriteable non-volatilememory, include but are not limited to memory cards; and media with abuilt-in ROM, including but not limited to ROM cassettes; etc.Furthermore, various information regarding stored images, for example,property information, may be stored in any other form, or it may beprovided in other ways.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. Shared processor hardware encompasses asingle microprocessor that executes some or all code from multiplemodules. Group processor hardware encompasses a microprocessor that, incombination with additional microprocessors, executes some or all codefrom one or more modules. References to multiple microprocessorsencompass multiple microprocessors on discrete dies, multiplemicroprocessors on a single die, multiple cores of a singlemicroprocessor, multiple threads of a single microprocessor, or acombination of the above.

Shared memory hardware encompasses a single memory device that storessome or all code from multiple modules. Group memory hardwareencompasses a memory device that, in combination with other memorydevices, stores some or all code from one or more modules.

The term memory hardware is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium is therefore considered tangible and non-transitory. Non-limitingexamples of the non-transitory computer-readable medium include, but arenot limited to, rewriteable non-volatile memory devices (including, forexample flash memory devices, erasable programmable read-only memorydevices, or a mask read-only memory devices); volatile memory devices(including, for example static random access memory devices or a dynamicrandom access memory devices); magnetic storage media (including, forexample an analog or digital magnetic tape or a hard disk drive); andoptical storage media (including, for example a CD, a DVD, or a Blu-rayDisc). Examples of the media with a built-in rewriteable non-volatilememory, include but are not limited to memory cards; and media with abuilt-in ROM, including but not limited to ROM cassettes; etc.Furthermore, various information regarding stored images, for example,property information, may be stored in any other form, or it may beprovided in other ways.

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

Although described with reference to specific examples and drawings,modifications, additions and substitutions of example embodiments may bevariously made according to the description by those of ordinary skillin the art. For example, the described techniques may be performed in anorder different with that of the methods described, and/or componentssuch as the described system, architecture, devices, circuit, and thelike, may be connected or combined to be different from theabove-described methods, or results may be appropriately achieved byother components or equivalents.

Although the present invention has been shown and described with respectto certain example embodiments, equivalents and modifications will occurto others skilled in the art upon the reading and understanding of thespecification. The present invention includes all such equivalents andmodifications and is limited only by the scope of the appended claims.

We claim:
 1. A computer-implemented method, comprising: obtainingmultiple magnetic resonance imaging (MRI) images, wherein the multipleMRI images are obtained from frequency scout measurements associatedwith multiple frequency offsets from a reference frequency of amagnetization excitation pulse; determining, from the multiple MRIimages, at least one reference image based on spectrum information ofeach of the multiple MRI images; determining, for each respective MRIimage of the multiple MRI images, a respective similarity measure basedon a comparison between the at least one reference image and therespective MRI image; and selecting, based on the similarity measures, afrequency offset for a subsequent MRI scan from the multiple frequencyoffsets.
 2. The method of claim 1, wherein said selecting of thefrequency offset for the subsequent MRI scan comprises: Selecting, fromthe multiple MRI images, a MRI image associated with a maximumsimilarity measure, wherein the MRI image is acquired in a frequencyscout measurement using the selected frequency offset.
 3. The method ofclaim 1, further comprising: determining the similarity measures using atrained machine learning algorithm.
 4. The method of claim 3, furthercomprising: generating, via the trained machine learning algorithm, arespective weighting map for each of the multiple MRI images, therespective weighting map penalizing signal deviations from a pixel-wisemedian that is calculated based on the at least one reference image, andwherein the respective similarity measure is determined based on therespective weighting map.
 5. The method of claim 1, further comprising:determining a region of interest (ROI) of each of the multiple MRIimages, wherein the spectrum information of each of the multiple MRIimages is associated with the ROI.
 6. The method of claim 5, whereinsaid determining of the ROI is based on a trained segmentation machinelearning algorithm for segmenting the ROI from at least one of themultiple MRI images.
 7. The method of claim 5, wherein the comparison isrestricted to the ROI.
 8. The method of claim 1, further comprising:determining, from the multiple MRI images, a subset of the multiple MRIimages based on the spectrum information of each of the multiple MRIimages, the subset including two or more MRI images, wherein the atleast one reference image is determined based on the two or more MRIimages.
 9. The method of claim 8, wherein said determining of the atleast one reference image comprises: averaging the two or more MRIimages.
 10. The method of claim 1, further comprising: applying ahigh-pass filter to each of the multiple MRI images, wherein thespectrum information of each of the multiple MRI images includes ahigh-frequency signal contribution determined based on said applying ofthe high-pass filter.
 11. The method of claim 8, wherein the two or moreMRI images have a minimum high-frequency signal contribution.
 12. Themethod of claim 1, wherein the subsequent MRI scan is based on asteady-state free precession pulse sequence using the selected frequencyoffset.
 13. The method of claim 1, wherein the subsequent MRI scan isone of a cardiac imaging scan, a fetal imaging scan, or an abdominalimaging scan.
 14. A computing device comprising: at least one processor;and a memory storing program code that, when executed at the at leastone processor, causes the computing device to perform the method ofclaim
 1. 15. An MRI scanner comprising the computing device of claim 14.16. The method of claim 2, further comprising: determining thesimilarity measures using a trained machine learning algorithm.
 17. Themethod of claim 16, further comprising: generating, via the trainedmachine learning algorithm, a respective weighting map for each of themultiple MRI images, the respective weighting map penalizing signaldeviations from a pixel-wise median that is calculated based on the atleast one reference image, and wherein the respective similarity measureis determined based on the respective weighting map.
 18. The method ofclaim 2, further comprising: determining a region of interest (ROI) ofeach of the multiple MRI images, wherein the spectrum information ofeach of the multiple MRI images is associated with the ROI.
 19. Themethod of claim 6, wherein the comparison is restricted to the ROI. 20.The method of claim 9, wherein the two or more MRI images have a minimumhigh-frequency signal contribution.